Adult suicide rates in the United States rose by almost 30 percent between 1999 and 2010. These rates have not markedly improved in decades. To date, previous suicide attempts and psychiatric diagnoses are largely the only known clinical risk factors for suicide death. Recent research shows that most individuals who die by suicide make a health care visit in the weeks and months prior to their death. Most of these visits occur in primary care or outpatient medical specialty settings. However, over half of these visits do not include a psychiatric diagnosis. Thus, there is limited evidence available from health care users in the US general population to inform targeted suicide screening and risk identification efforts in general medical settings. New research is needed to investigate the general medical clinical factors associated with suicide risk among individuals without a known risk factor. This research project uses data on more than 4000 individuals who died by suicide and made health care visits to one of eight health care systems across the United States in the year prior to their death. These health systems are members of the Mental Health Research Network and have affiliated health plans. They are able to capture nearly all health care for their patients via the Virtual Data Warehouse (VDW). The VDW consists of electronic medical record and insurance claims data organized using standardized data structures and definitions across sites. These data are matched with official regional mortality data. This project includes the following Specific Aims: 1) Identify the types and timing of clinical factors prior to suicide, 2a) Compare clinical factors before suicide to a matched sample of health care users, 2b) Detect associations between additional clinical factors and suicide, 3) Develop a prediction model of clinical factors prior to suicide, and 4) investigate indicators of hidden mental health need in general medical chart notes prior to suicide. We employ a case-control study approach to test specific hypotheses, while also using novel environment-wide association study methods and latent class analysis to detect new risk factors. We develop a prediction model of clinical factors and suicide. We use qualitative analyses to review clinical text notes and develop a natural language processing algorithm to identify risk. Clinical factors to be studied include medical diagnoses, medications, health care procedures, and types of health care visits. These results will inform decisions about how to focus suicide prevention in medical settings and provide information in response to the 2012 National Action Alliance for Suicide Prevention and US Surgeon General report.